ArticleBMJ open2019
Can online self-reports assist in real-time identification of influenza vaccination uptake? A cross-sectional study of influenza vaccine-related tweets in the USA, 2013-2017.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Predictors of childhood vaccination and recent influenza vaccination in older Brazilian adults: an analysis using conventional regression and machine learning approaches.Frontiers in public health · 2026Article
- Factors influencing the COVID-19 vaccine uptake among Ukrainian refugees in Poland: a cross-sectional study.Frontiers in public health · 2026Article
- Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance.Social science & medicine (1982) · 2024Article
- Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.Journal of medical Internet research · 2024Article
- Unraveling attributes of COVID-19 vaccine acceptance and uptake in the U.S.: a large nationwide study.Scientific reports · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- Social media and internet search data to inform drug utilization: A systematic scoping review.Frontiers in digital health · 2023Article
- Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic.Annals of operations research · 2022Article
- Evaluation of clustering and topic modeling methods over health-related tweets and emails.Artificial intelligence in medicine · 2021Article
- Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement.Proceedings of the ACM on human-computer interaction · 2021Article
- Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Clustering and topic modeling over tweets: A comparison over a health dataset.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
- From data to decisions: The vital role of digital epidemiology for strengthening public health.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
introductionThe Centers for Disease Control and Prevention (CDC) spend significant time and resources to track influenza vaccination coverage each influenza season using national surveys. Emerging data from social media provide an alternative solution to surveillance at both national and local levels of influenza vaccination coverage in near real time.
objectivesThis study aimed to characterise and analyse the vaccinated population from temporal, demographical and geographical perspectives using automatic classification of vaccination-related Twitter data.
methodsIn this cross-sectional study, we continuously collected tweets containing both influenza-related terms and vaccine-related terms covering four consecutive influenza seasons from 2013 to 2017. We created a machine learning classifier to identify relevant tweets, then evaluated the approach by comparing to data from the CDC's FluVaxView. We limited our analysis to tweets geolocated within the USA.
resultsWe assessed 1 124 839 tweets. We found strong correlations of 0.799 between monthly Twitter estimates and CDC, with correlations as high as 0.950 in individual influenza seasons. We also found that our approach obtained geographical correlations of 0.387 at the US state level and 0.467 at the regional level. Finally, we found a higher level of influenza vaccine tweets among female users than male users, also consistent with the results of CDC surveys on vaccine uptake.
conclusionSignificant correlations between Twitter data and CDC data show the potential of using social media for vaccination surveillance. Temporal variability is captured better than geographical and demographical variability. We discuss potential paths forward for leveraging this approach.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.